Machine learning model representation and execution

ABSTRACT

Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a Machine Learning (ML) modelcontainer builder.

BACKGROUND

For many ML platforms, Data Scientists have often developed models torequire manual steps to be performed in a specific order to produce acertain output. Additionally, when copying the ML model to anothercomputer, the ML model application could fail due to differences in theconfiguration of the two systems. Hence, reproducing, building andrunning a ML model created by another Data Scientist is challenging.

Additionally, isolation is a major challenge when running a usersubmitted model on an external runtime within a larger platform.Isolation is necessary when running user supplied software to protectagainst both vulnerabilities and deliberately malicious software.Isolation also protects the platform from software bugs in the usersubmitted model that could consume too many resources, run indefinitely,or otherwise cause the application to fail.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a container 200 for an ML model functioning within thecommunication network of FIG. 1 in accordance with various aspectsdescribed herein.

FIG. 2B is a block diagram illustrating an example, non-limitingembodiment of a process 220 for an ML model system functioning withinthe communication network of FIG. 1 in accordance with various aspectsdescribed herein.

FIG. 2C is a block diagram illustrating an example, non-limitingembodiment of an execution process for an ML model image 224 functioningwithin the communication network of FIG. 1 in accordance with variousaspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for a builder of a ML model container. Other embodiments aredescribed in the subject disclosure.

One or more aspects of the subject disclosure include a device,including a processing system including a processor; and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations including receiving userspecified metadata for execution tasks associated with a machinelearning (ML) model; receiving artifacts specifying binary library filesand program code for implementing the ML model; creating a file systemstructure for a container to hold the ML model; receiving environmentvariables for operation of the ML model; and building the containerincluding a model image for the ML model.

One or more aspects of the subject disclosure include a machine-readablemedium with executable instructions that, when executed by a processingsystem including a processor, facilitate performance of operations forcreating a file system structure for a container to hold a machinelearning (ML) model; receiving environment variables for operation ofthe ML model; layering a model image for the ML model on a base image inthe container; copying artifacts into the container; and installingdependencies of the ML model into the container using a package manager.

One or more aspects of the subject disclosure include a method includingsteps of: layering, by a processing system including a processor, a baseimage over a generic container; adding, by the processing system,user-specified packages wherein the user-specified packages areinstalled by a package manager; and layering, by the processing system,program code for a machine learning (ML) model.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in part receivinguser specified metadata for execution tasks associated with a machinelearning (ML) model; receiving artifacts specifying program code forimplementing the ML model; creating a file system structure for acontainer to hold the ML model; and receiving environment variables foroperation of the ML model. In particular, a communications network 125is presented for providing broadband access 110 to a plurality of dataterminals 114 via access terminal 112, wireless access 120 to aplurality of mobile devices 124 and vehicle 126 via base station oraccess point 122, voice access 130 to a plurality of telephony devices134, via switching device 132 and/or media access 140 to a plurality ofaudio/video display devices 144 via media terminal 142. In addition,communication network 125 is coupled to one or more content sources 175of audio, video, graphics, text and/or other media. While broadbandaccess 110, wireless access 120, voice access 130 and media access 140are shown separately, one or more of these forms of access can becombined to provide multiple access services to a single client device(e.g., mobile devices 124 can receive media content via media terminal142, data terminal 114 can be provided voice access via switching device132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a container 200 for an ML model functioning within thecommunication network of FIG. 1 in accordance with various aspectsdescribed herein. A ML model is defined as a combination of executablecode and metadata. By confining each ML model within a single, isolatedcontainer, the system provides robust support for interaction, filesystem sharing, networking, support for pipes, and environmentvariables. As shown in FIG. 2A, the container comprises a genericcontainer 201, a base layer 202, and a user layer 210 comprising one ormore packages 211, 212, and user artifacts 215.

The generic container 201 represents the platform on which the entirestack illustrated in FIG. 2A is constructed. In an embodiment, thegeneric container 201 is a basic docker container. Docker isconsiderably simpler to share information for the ML model using thefile system, network, pipes, or environment variables when that ML modelis a docker container instead of a full virtual machine. See, e.g.,https://wwww.zdnet.com/article/what-is-docker-and-why-is-it-so-darn-popular/,which is incorporated by reference herein.

The base layer 202 is layered on top of the generic container. The baselayer 202 comprises a package manager 203, hardware specifications 204,ML toolkits 205, a repository manager 206 and a programming language207.

The package manager 203 is used for the specification of dependenciesacross multiple languages. In an embodiment, the package manager isconda, the package manager for Anaconda. See, e.g.,http://en.wikipedia.org/wiki/Conda_(package_manager), which isincorporated by reference herein. Enumeration of dependencies areincluded to define specific package managers to handle certain packages.Dependencies are given to the system as requirements files matching aspecific naming convention. For example, a requirements file for theAnaconda package manager, miniconda, would expect a file namedconda_requirements.txt. Miniconda is a small, bootstrap version ofAnaconda that includes only conda, Python, the packages they depend on,and a small number of other useful packages, including pip, zlib and afew others. See, e.g., https://docs.conda.io/en/latest/miniconda.html,which is incorporated by reference herein. If the model requires Python3.7 and SciKit Learn 0.21.2, the content of the requirements file wouldbe:

-   -   python=3.7    -   scikit-learn=0.21.2

The hardware specifications 204 define a minimum and maximum overallhardware capabilities profile required for the ML model to functionwithin. The hardware specifications indicate hardware resources such asa number and type of central processing units, a number and type ofgraphics processing units (GPUs), an amount of memory, an amount ofvolatile memory, an amount of storage, etc. For example, GPU support canbe handled through the inclusion of a CUDA runtime and other associatedtools. See, e.g.,https://graphicscardhub.com/cuda-cores-vs-stream-processors/, which isincorporated by reference herein.

The ML toolkits 205 represents object code packages commonly used by MLmodel code. For example, the programming language Python can beinstalled along with some extremely common ML toolkits like scikit-learnand other libraries of that nature.

The repository manager 206 is a server that stores and retrieves files,which are referred to as user artifacts 215. The source code written toimplement a ML model is often dependent on external libraries. Forexample, in Java, these libraries are stored in binary files calledJARs. The primary use of a repository manager is to proxy and cacheartifacts from “external” repositories. See, e.g.,https://blog.sonatype.com/2010/04/why-nexus-for-the-non-programmer/,which is incorporated by reference herein. The repository managerenables collaboration between users, provides and stores artifacts, andassigns a standard coordinate system to the artifacts stored. Therepository manager 206 provides an available facility for cataloging andstoring artifacts using the same “numbering” system that the libraryuses. When a group of Data Scientists develop a new ML model or alibrary, they submit it to the repository manager 206.

In an embodiment, the repository manager 206 is an internal Nexusinstance. Proxies to many remote package sources such as the pythonpackage index, anaconda, conda-forge, and so on are managed by therepository manager 206. These images never actually make direct internetconnections but are instead routed through internally hostedrepositories when resolving dependencies.

The programming language 207 are those languages supported by the systemto program the ML model. Such languages include those typically used byData Scientists, such as Python, R, Java/Scala and Julia. In a preferredembodiment, the programming language 207 can be an interpretiveprogramming language such as Python or R, although programming language207 may represent standardized byte code executable machines, such aswith Java. The code portion of a ML model may be of any format,regardless of whether it is directly represented as an executable andlinkable format (ELF) or interpreted (Python, R, Java). See, e.g.,https://en.wikipedia.org/wiki/Executable_and_Linkable_Format, which isincorporated by reference herein. In the case of interpreted code, themetadata should indicate the required interpreter.

The packages 211, 212 are specified by the user artifacts 215. Forexample, the first layer of the user layer 210 at the individual modellevel can be conda and pip packages. These packages are handled byinspecting the user artifacts 215 that are specified by the user. If afile named conda_requirements.txt or pip_requirements.txt is present,these packages will be installed by their corresponding packagemanagement system and compose this layer.

The user artifacts 215 are specified by the user and must include themodel specific code as well as any other binary file required for thesuccessful running of their code. An example Python model might haveartifacts like:

-   -   housing_gbm.py—Script used to run the model from Python.    -   housing_gbm.pkl—Serialized representation of estimator/pipeline.    -   categorical_type_a.csv—Enumeration of values required by model.

For example, the user specified metadata may include a train command, arun command, and dependencies. The train command is a shell command thatinvokes execution of the ML model code for training:

python housing_gbm.py --model-arg 1 TRAINThe run command is a shell command that invokes execution of the MLmodel code for running the trained ML model:python housing_gbm.py --model-arg 1 TESTThe dependencies are noted above in paragraph [00028].

FIG. 2B is a block diagram illustrating an example, non-limitingembodiment of a process 220 for an ML model system functioning withinthe communication network of FIG. 1 in accordance with various aspectsdescribed herein. As shown in FIG. 2B, a ML model managing system 221will use environment variables 222 and a data volume 223 to create theML model image 224. The ML model image 224 will use a predictions volume225 to provide output data to the ML model managing system 221.

The ML model image 224 must reference the environment variables 222 todetermine how an operation should occur. All the following environmentvariables should be handled by the ML model image 224:

-   -   training_path—Absolute path name of the file containing data to        be used for training the ML model.    -   testing_path—Absolute path name of the file containing data to        be used for testing/evaluating the ML model.    -   intermediate_path—Absolute path name of a directory that can be        used by the model to store data for subsequent stages. This is        commonly used in the training phase to store a serialized        version of the trained model for use within the testing phase. A        .pkl file is valid and used for this purpose quite frequently,        though it is not the only format used for this purpose. See,        e.g., https://fileinfo.com/extension/pkl, which is incorporated        by reference herein.    -   prediction_path—Identifies the absolute path name for the file        the ML model should write output results to.    -   artifact_path—The absolute path name of the model artifacts        folder containing all the ML model artifacts.    -   id_column—Specifies a column within the training or testing data        that uniquely identifies each record.    -   target_column—The column for which the ML model should train and        generate predictions for.

Volumes in the container 200 has the following model specific portions:

-   -   /model—a storage location embedded directly within the model        container at build time        -   /model/artifacts—a folder that will contain all artifacts            submitted by the user. This will also be the working            directory any time a command is issued.    -   /data—Binding location for a volume to supply data to be        processed by model.    -   /intermediate—Binding location for a volume to provide        non-volatile storage for the ML model between execution phases.

The predictions volume 225 in the container 200 has the following modelspecific portions:

-   -   /predictions—Binding location for a volume to store outputs.

Once a user has specified the artifacts and metadata of a ML model, theML model image 224 can then be constructed. During the build process,the container 200 will have network connectivity for the purpose ofinstalling dependencies—no user code will be running. During all runphases, network connectivity will not be available. This is primarily asecurity measure to prevent user executable code from having networkaccess, which could create many potential security issues. One examplewould be if the user's ML model includes a command and control typesystem that would essentially give an external network access to theinternal network (i.e., the internal network running in the container200). Installing dependencies is handled by the system reading thedependencies wanted and the system performs the network access on behalfof the container 200, not by the user code. Additionally, by forcingdependencies to be bundled within the container 200 at build time, thedependencies are guaranteed to be installed only one single time insteadof each invocation of the model.

The ML model image 224 process 220 comprises these three main steps:

-   -   Reference base image—The first step layers a user's ML model        onto an existing base image 202. The base image 202 provides        OS-level functionality, exposes usable package managers 203, and        any built-in ML toolkits 205 or programming languages 207. See        paragraphs [00028], [00030] and [00033], above.    -   Copy artifacts—The second step copies all user artifacts 215        into the container 200.    -   Install dependencies—Installs model dependency packages 211, 212        using the appropriate package manager 203.

In an embodiment, Docker is used to isolate individual ML models withinsingle containers. One of the key reasons for choosing Docker was therobustness of interaction supported within Docker containers versus avirtualized infrastructure. It's considerably simpler to shareinformation of an ML model using a file system, network, pipes, orenvironment variables when that model is a Docker container instead of afull virtual machine.

For communication with the ML model image 224, the environment variables222 are used to transmit state and file system volumes (such as the datavolume 223 and the predictions volume 225) to both transmit and receivedata between the ML model managing system 221 and the ML model image224. The data is transferred to the host system that will be executingthe ML model image 224 before running a phase within the container 200.When running the container 200, the data on the host is bound as thedata volume 223 within the container 200 (see,https://docs.docker.com/storage/bind-mounts/, which is incorporated byreference herein). The ML model image 224 running within the container200 can access the data volume 223 using standard file system functions.

FIG. 2C is a block diagram illustrating an example, non-limitingembodiment of an execution process for an ML model image 224 functioningwithin the communication network of FIG. 1 in accordance with variousaspects described herein. The running of a ML model image 224 consistsof both a train phase 230 and test phase 240, as shown in FIG. 2C. Thesephases are provided by the user in the form of a command line that willbe used within their container to run each phase. The only phaserequired to run is the test phase 240, so if it doesn't make sense tohave the train phase 230, then that phase can be omitted; however, it isgenerally recommended that both phases are specified. See paragraph[00036] above regarding commands for each phase.

The train phase 230 comprises the steps of transforming the data 231,the ML model fitting to the data 232 and serializing 233 the ML model.During the training phase, the ML model will be presented with datacontaining actual values to train from. The ML model will be responsiblefor reading that data and fitting to the data 232. Model fitting is ameasure of how well a machine learning model generalizes to similar datato that on which it was trained. See, e.g.,https://www.datarobot.com/wiki/fitting/, which is incorporated byreference herein. In general, this trained representation will beserialized 233 to the intermediate_path for subsequent deserializationduring the test phase. See paragraph [00038] above. Once done with theserialization step, the model will terminate and be reinvoked within anew process, perhaps even on a different compute node.

The container 200 will be unloaded between each phase and requires theML model image 224 to write state to the intermediate path, which the MLmodel managing system 221 maintains between phases. The intermediatevolume provides the container 200 with the ability to persist data forlater consumption during the test phase 240. See paragraph [00039]above. When the ML model image 224 completes a phase, the ML model image224 should terminate and issue a return code of 0.

The test phase 240 comprises the steps of transforming the data 241,deserializing 242 the trained model, computing predictions 243 andoutputting 244 the predictions. The testing phase will present the modelwith data that does not contain target values. Typically, the systemperforms deserializing 242 the trained ML model representation from theintermediate volume, computing predictions 243 of the target values inmemory, and outputting 244 those predictions to the predictions volume225 that can be used to capture that output from the ML model.

To allow the most flexibility, a ML model does not have to explicitlydefine a training phase. Instead, the model can optionally perform bothsteps within the testing phase directly. While not recommended, thetesting phase can expose both training/testing data and variables toskip the training phase.

Diagnostics are supported by capturing model specific output as well asruntime output. From the ML model's perspective, all informational anderror output should be directed to the standard output and standarderror pipes. Any data across these pipes will be captured and madeavailable to assist in debugging model or runtime errors.

The predictions generated by running a ML model should be directed tothe file specified by the prediction_path environment variable. Afterrunning the test phase, the system will extract the file directly. Theinput data will always include a unique row_id column to identifyindividual records. During the training phase, the ML model will begiven a dataset containing the following [id, feature1 . . . n, target].This allows the ML model to fit the ML model according to ‘real’ data.During the testing phase, the ML model is presented with [id, feature1 .. . n] and the ML model is expected to supply the target value for thegiven features. Internally, the system has the real target value, sowhen the ML model gives back the estimated target value, the two valuescan be compared to evaluate the performance of the model. A model thatis well-fitted produces more accurate outcomes. A model that isoverfitted matches the data too closely. A model that is underfitteddoesn't match closely enough.

The row_id is being included to disambiguate which record the ML modelis providing a target value estimate for. The format of this file is aCSV with two columns, the id_column and the target_column. For example:

row_id, value 1, 500 2, 600 3, 350 5, 700 6, 400

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2B and2C, it is to be understood and appreciated that the claimed subjectmatter is not limited by the order of the blocks, as some blocks mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. A virtualizedcommunication network is presented that can be used to implement some orall the subsystems and functions of communication network 100, thesubsystems and functions of ML model managing system 221, and methods220, 230 and 240 presented in FIGS. 1, 2A, 2B, 2C and 3. For example,virtualized communication network 300 can facilitate in whole or in partreceiving user specified metadata for execution tasks associated with amachine learning (ML) model; receiving artifacts specifying program codefor implementing the ML model; creating a file system structure for acontainer to hold the ML model; and receiving environment variables foroperation of the ML model.

A cloud networking architecture is shown that leverages cloudtechnologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so, the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In some cases, anetwork element needs to be positioned at a specific place, and thisallows for less sharing of common infrastructure. Other times, thenetwork elements have specific physical layer adapters that cannot beabstracted or virtualized and might require special DSP code and analogfront ends (AFEs) that do not lend themselves to implementation as VNEs330, 332 or 334. These network elements can be included in transportlayer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross several servers—each of which adds a portion of the capability,and overall, which creates an elastic function with higher availabilitythan its former monolithic version. These virtual network elements 330,332, 334, etc. can be instantiated and managed using an orchestrationapproach like those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Networkworkloads may have applications distributed across the virtualizednetwork function cloud 325 and cloud computing environment 375 and inthe commercial cloud or might simply orchestrate workloads supportedentirely in NFV infrastructure from these third-party locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. Computing environment 400 can beused in the implementation of network elements 150, 152, 154, 156,access terminal 112, base station or access point 122, switching device132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of thesedevices can be implemented via computer-executable instructions that canrun on one or more computers, and/or in combination with other programmodules and/or as a combination of hardware and software. For example,computing environment 400 can facilitate in whole or in part receivinguser specified metadata for execution tasks associated with a machinelearning (ML) model; receiving artifacts specifying program code forimplementing the ML model; creating a file system structure for acontainer to hold the ML model; and receiving environment variables foroperation of the ML model.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform tasks or implement abstract data types.Moreover, those skilled in the art will appreciate that the methods canbe practiced with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, minicomputers,mainframe computers, as well as personal computers, hand-held computingdevices, microprocessor-based or programmable consumer electronics, andthe like, each of which can be operatively coupled to one or moreassociated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

Several program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology like that used in a cell phone that enables suchdevices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance like the basic 10BaseT wired Ethernet networksused in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part receiving user specified metadata for executiontasks associated with a machine learning (ML) model; receiving artifactsspecifying program code for implementing the ML model; creating a filesystem structure for a container to hold the ML model; and receivingenvironment variables for operation of the ML model. In one or moreembodiments, the mobile network platform 510 can generate and receivesignals transmitted and received by base stations or access points suchas base station or access point 122. Generally, mobile network platform510 can comprise components, e.g., nodes, gateways, interfaces, servers,or disparate platforms, that facilitate both packet-switched (PS) (e.g.,internet protocol (IP), frame relay, asynchronous transfer mode (ATM))and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. As anon-limiting example, mobile network platform 510 can be included intelecommunications carrier networks and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 510comprises CS gateway node(s) 512 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 540 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 canauthorize and authenticate traffic (e.g., voice) arising from suchnetworks. Additionally, CS gateway node(s) 512 can access mobility, orroaming, data generated through SS7 network 560; for instance, mobilitydata stored in a visited location register (VLR), which can reside inmemory 530. Moreover, CS gateway node(s) 512 interfaces CS-based trafficand signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 512 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 512, PSgateway node(s) 518, and serving node(s) 516, is provided and dictatedby radio technology(ies) utilized by mobile network platform 510 fortelecommunication over a radio access network 520 with other devices,such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform tasks and/orimplement abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part receiving userspecified metadata for execution tasks associated with a machinelearning (ML) model; receiving artifacts specifying program code forimplementing the ML model; creating a file system structure for acontainer to hold the ML model; and receiving environment variables foroperation of the ML model.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high-volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x₁, x₂, x₃, x₄ . . .x_(n)), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. Yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants distinctions among the terms. It should be appreciated thatsuch terms can refer to human entities or automated components supportedthrough artificial intelligence (e.g., a capacity to make inferencebased, at least, on complex mathematical formalisms), which can providesimulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates an ordering of steps, other orderings arelikewise possible provided that the principles of causality aremaintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: receiving user specified metadatafor execution tasks associated with a machine learning (ML) model;receiving artifacts specifying program code for implementing the MLmodel; creating a file system structure for a container to hold the MLmodel; receiving environment variables for operation of the ML model;and building the container including a model image for the ML model. 2.The device of claim 1, wherein the user specified metadata comprises ashell command for training the ML model.
 3. The device of claim 1,wherein the user specified metadata comprises a shell command forexecuting the ML model.
 4. The device of claim 1, wherein the userspecified metadata comprises instructions to a package manager.
 5. Thedevice of claim 1, wherein the user specified metadata compriseshardware capabilities for executing the ML model.
 6. The device of claim5, wherein the hardware capabilities for executing the ML model comprisea number and type of central processing units, a number and type ofgraphics processing units, an amount of memory, an amount of volatilememory, an amount of storage, or a combination thereof.
 7. The device ofclaim 1, wherein the artifacts include binary library files needed toimplement the ML model.
 8. The device of claim 1, wherein the artifactsinclude an enumeration of values required by the ML model.
 9. The deviceof claim 1, wherein the file system structure comprises a path to amodel artifacts folder, a data volume, and intermediate volume, apredictions volume, or a combination thereof.
 10. The device of claim 1,wherein the environment variables include a first path to a first filecontaining data for training the ML model, a second path to a secondfile containing data for testing the ML model, a third path to anintermediate volume to provide non-volatile storage for the ML model, afourth path to a third file comprising output results, or a combinationthereof.
 11. The device of claim 1, wherein the container has networkconnectivity during the building of the model image.
 12. The device ofclaim 1, wherein the building comprises layering the ML model onto abase image.
 13. The device of claim 12, wherein the building furthercomprises copying the artifacts into the container.
 14. The device ofclaim 13, wherein the building further comprises installing dependenciesof the ML model into the container using a package manager.
 15. Thedevice of claim 13, wherein the processing system comprises a pluralityof processors operating in a distributed computing environment.
 16. Amachine-readable medium, comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations, the operations comprising: creating a filesystem structure for a container to hold a machine learning (ML) model;receiving environment variables for operation of the ML model; layeringa model image for the ML model on a base image in the container; copyingartifacts into the container; and installing dependencies of the MLmodel into the container using a package manager.
 17. Themachine-readable medium of claim 16, wherein the artifacts compriseprogram code for the ML model, binary files needed to execute theprogram code for the ML model, or a combination thereof.
 18. Themachine-readable medium of claim 17, wherein the processing systemcomprises a plurality of processors operating in a distributed computingenvironment.
 19. A method, comprising: layering, by a processing systemincluding a processor, a base image over a generic container; adding, bythe processing system, user-specified packages wherein theuser-specified packages are installed by a package manager; andlayering, by the processing system, program code for a machine learning(ML) model.
 20. The method of claim 19, wherein the base image comprisesthe package manager, hardware specifications, a programming language, MLtoolkits, a repository manager, or a combination thereof.